The present invention relates to a normalizer based on a Least Mean Square (LMS) adaptive algorithm which is configured to provide effective normalization when the background noise is locally non-stationary and when the target may be subject to time spread of unknown extent.
FIG. 1 is a block diagram of a typical active sonar system 20. A waveform, such as a pulsed CW or FM, is transmitted by the transmitter 22 via a transmit array 24, which may be co-located with the receiver elements or physically separated. The waveform echo is detected by a matched filter processor, as shown. An array 26 of hydrophones is used to form a beam, having directional response in the direction of interest and low response elsewhere, in the direction of the target. The processing to form such a beam is generally known as a beamformer. The beamformer 28 output is passed to a matched filter 30, which correlates the received signals with a replica of the transmitted signal. The magnitude squared (32) of the matched filter output exhibits a peak at the location of any reflection present in the beamformer output. The time series at the matched filter output can be viewed as a plot of received energy versus range. Typically, the matched filter output will not only exhibit peaks due to target echoes, but those due to background noise, reflections from objects such as bottom and surface, and reverberation, which is a more diffuse reflection of the sound from the many small scatterers in the ocean.
The detection process 36 consists of comparing received signal plus noise to a threshold, and declaring a detection wherever the threshold is exceeded. If this threshold is applied directly at the matched filter output, the number of threshold crossings reflects the variations of noise power with range. This variation can be very significant in many environments. An objective of most active sonar signal processors is to achieve Constant False Alarm Rate (CFAR) operation, in which the number of false alarms (or detections of non-target echoes or noise) is more or less constant with range. This is usually achieved by processing the matched filter output through a normalizer 34, as shown in FIG. 1, prior to the threshold test. The normalizer 34 estimates the noise background power by examining the noise background in the vicinity of each range cell, then divides the power in that cell by the noise estimate. The normalizer output is therefore nominally a signal-to-noise ratio in cells containing signal, and nominally unity in noise cells. Consequently, if the normalizer effectively estimates the noise power, the number of false alarms will be constant with range.
Range normalizers are known which estimate the noise power by examining range cells near the cells of interest. A range gap is left between the signal bin and the bins used to estimate the noise in order to assure that signal components from a range spread signal do not corrupt the noise measurement. The estimate is based on the power in two noise windows containing N/2 cells on either side of the cell of interest, as shown in FIG. 2. Generally, a pass through the data in the noise windows is made to eliminate cells with large power (since they may be signal components and would corrupt the noise estimate). N must be chosen large enough to give a good estimate of the noise background. However, since each bin represents a range cell, using many range bins makes the data used in the estimate further away from the cell of interest, so that it may not be representative of the local noise, particularly in environments with a lot of variability, like shallow water. This can reduce the effectiveness of the normalizer in producing CFAR operation.
The signal can also be spread in many environments, including shallow water, so that it appears in a number of range cells. In conventional systems, this is handled by summing a number of range cells to form the signal output, often referred to as over-averaging. This combines the total signal energy into a single output cell if the size of the signal window is matched to the signal spread, as shown in FIG. 3. If the window is too large, noise is summed with the signal, while all the signal energy is not combined if the window is too small. The size of the window must therefore be set in the conventional active sonar, and may not match the signal spread in variable environments.
In active sonars it is desirable to operate at a constant false alarm rate (CFAR) in order to avoid overloading the operator. The false alarm rate is determined by the statistics of the noise in the absence of target, as well as the detection threshold applied at the matched filter-energy detector output. The process of estimating the noise statistics and using the estimate to set the detection threshold is usually referred to as normalization, and is equivalent to using the noise estimate to scale the incoming data, which is compared to a fixed threshold.
The goal of the normalizer in an active sonar system is to normalize the background in order to provide a CFAR in the presence of noise only, while providing detection of signals of interest. The purpose of this invention is to provide these functions when the background noise is locally non-stationary and when the target may be subject to time spread of unknown extent. In existing normalizers this is done either by attempting to exclude data samples that are "outliers" inconsistent with the overall statistics (which does not directly address non-stationary statistics) or by non-parametric techniques, which attempt to be insensitive to statistics (at some sacrifice in performance). The LMS normalizer in accordance with this invention does this by dynamically determining the signal and noise statistics using an adaptive algorithm which attempts to minimize the ratio of the power in a signal bin to that computed in a noise averaging window, subject to a constraint that assures that the signal will be passed by the normalizer.
Numerous normalization algorithms currently exist for use in radar and active sonars. The most widely used are cell averaging (CA) normalizers, which compute the average noise power in averaging windows adjacent to the signal bin of interest, and then use this average to normalize the signal bin, as described in "A performance comparison of four noise background normalization schemes proposed for signal detection systems," W. A. Struzinski and E. D. Lowe, J. Accoust. Soc. Am., Vol. 786, No. 6, December 1984. A variation of this structure that is simpler to implement and operates over a larger dynamic range than the CA normalizer, but yields slightly degraded performance is the LGO/CFAR normalizer, described in "Detection Performance of the Cell Averaging LOG/CFAR Receiver," V. G. Hansen and H. R. Ward, IEEE Trans. Aerospace and Electronics Systems, Vol. AES-8, No. 5, September 1972. This normalizer implements a CA average on the logarithm of the data to be normalized. These normalizer structures assume 1) stationary statistics on the noise background, and 2) that the noise averaging windows contain only noise (no signal). Their performance can be severely degraded if these assumptions are invalid.
Several approaches have been taken into account for background non-stationarity and the presence of signal in the noise windows (as can occur with spread targets or in a multitarget environment). One approach is to use a multiple-pass normalization in conjunction with a CA algorithm. In multiple-pass techniques, the first noise average is used to set a threshold for the rejection of samples in the noise averaging window, usually followed by replacement of the values by the initial average. This is followed by a conventional normalizer pass on the resulting data. Usually only one of these so-called "outlier rejection" passes is used prior to normalization, but multiple passes can be used. The multiple-pass normalizers do not directly deal with non-stationary background, but assume that the background is uniform except for a few outliers and signals. Hence, when the background statistics show significant variation, the performance is severely degraded.
A second approach to non-stationary and unknown statistics has been the class of ordered statistic normalizers, as described in "Radar CFAR Thresholding Clutter and Multiple Target Situations," H. Rohling, IEEE Trans. Aerospace and Electronic Systems, Vol. AES-19. No. 5, July 1983. The more complex ordered statistic normalizers order the data in the noise windows by magnitude, then utilize the n.sup.th largest value (e.g., the median) to normalize. These normalizers have some advantages when there are multiple targets present or near the edges of regions where the noise is rapidly increasing, but can be noisier because they do not include averaging. Hybrid algorithms which perform some averaging prior to ordering the data can be used to overcome some of the increased noise in ordered statistic normalizers. The simplest of these are modifications of the CA normalizer known as the "greatest of" (GO) or "least of" (LO) normalizer. The GO (LO) normalizer separately computes the noise average in windows on either side of the signal bin, then uses the larger (smaller) of the averages to normalize the signal bin. These normalizers provide a compromise between the advantages of CA and ordered statistics normalizers. Ordered statistic normalizers are non-parametric processors that account for unknown or varying statistics by being relatively insensitive to them. Such processors sacrifice performance in a given environment in order to maintain acceptable performance over a range of environments.